For several weeks, I logged every moment when understanding our system required asking another person instead of reading code, documentation, dashboards, or configuration. What started as a small experiment turned into a surprisingly useful map of hidden knowledge dependencies. The biggest risk was not missing documentation. It was that some parts of the system effectively existed inside one engineer's head.

AI-generated code often looks cleaner than code written by a tired developer at 2 a.m. It has sensible names, neat layers, comments, tests and even error handling. The problem starts when the code meets something the prompt forgot to mention: two requests at once, a repeated webhook, a cancelled task or a database failure halfway through an operation.

This article is about bugs that stay invisible during a normal code review. I took several ordinary backend tasks, generated working solutions and then tried to break them with timing, retries and bad input. The code compiled. The tests were green. Some of it was still unsafe.

AIGirl is where to start, because its free tier works without a card and the paid plan is around $5.90 a month — the lowest entry here. SpicyChat has the clearest ladder from free to unlimited if you want to know exactly what each step costs. And OurDream is the pick if you want unlimited messaging alongside images and voice in one subscription rather than four separate bills.

Our office has roughly one hundred employees, but for a long time we had no proper help desk process. Requests arrived through email, corporate chat, phone calls, and random conversations in the hallway. It always felt as if most of the day was spent dealing with servers, networks, backups, and serious incidents.

To check whether that impression was accurate, I logged every support request for one month. I measured active work time, recorded the cause of recurring problems, tracked waiting periods, and counted how often a new request interrupted something already in progress.

Over 22 working days, I recorded 496 contacts. After merging duplicate reports related to the same issue, those contacts became 421 separate support episodes.

The most surprising result was not the total number of requests. It was the damage caused by short five‑minute tasks constantly breaking longer technical work into small, nearly useless fragments.

This article explains how I collected the data, why average resolution time is often misleading, how I automated Windows workstation diagnostics with PowerShell, and how I processed the resulting event log using Python and PostgreSQL.

Полгода назад я понял, что мы проигрываем не конкурентам и не техдолгу. Мы проигрываем времени суток. В этой статье — мой практический разбор того, как я замерял хронотипы команды, собирал телеметрию активности, анализировал коммиты и встречи, строил простенькую модель продуктивности и в итоге полностью перестроил рабочий график удалённой команды. Будет немного биологии, немного математики, немного кода и много личных факапов.